CN118071214B - Agricultural product planting traceability analysis management system and method based on big data - Google Patents
Agricultural product planting traceability analysis management system and method based on big data Download PDFInfo
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- 235000019198 oils Nutrition 0.000 claims description 98
- 239000002689 soil Substances 0.000 claims description 54
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Abstract
The invention belongs to the technical field of agricultural product planting analysis management, and particularly discloses an agricultural product planting traceability analysis management system and method based on big data, wherein the system comprises the following steps: the system comprises a peanut planting data extraction module, a historical traceability information extraction module, a peanut planting state analysis module, a planting quality traceability analysis module, a peanut planting adjustment judgment module, a peanut planting adjustment confirmation module, a planting information base and a peanut planting adjustment feedback terminal. The invention effectively solves the problem of insufficient attention to the current planting management of the agricultural product production body, ensures the management effect of the planting quality of the subsequent peanuts, expands the planting traceability information of the agricultural products, is convenient for timely finding and solving the problems in the peanut planting process, further improves the optimization effect of the subsequent planting of the peanuts, ensures the utilization rate of the information of the peanut planting and production processes, and realizes the complete information closed loop of the production process information and the planting links.
Description
Technical Field
The invention belongs to the technical field of agricultural product planting analysis management, and relates to an agricultural product planting traceability analysis management system and method based on big data.
Background
With the increasing attention of people to food safety and quality, agricultural product planting traceability analysis and management becomes an important issue in the agricultural field. The traceability analysis management is particularly important for peanut agricultural products. Not only can help farmers optimize planting management and improve yield and quality, but also can ensure the safety and quality traceability of peanut products.
At present, the planting traceability analysis management of peanut products mainly carries out planting management by tracking and recording key information in the whole peanut planting process from seed planting to harvesting, processing and selling.
According to the agricultural product planting-based traceability management system disclosed in the China patent application with the application publication number of CN114897544A, the purchasing data collection module, the planting and picking data collection module, the environment monitoring data collection module, the personnel operation data collection module, the product quality analysis data collection module and the sales information data collection module are arranged, so that information of agricultural products from planting to selling is comprehensively collected, the comprehensiveness and accuracy of agricultural product information storage are improved, links and responsibility bodies with problems are conveniently and accurately found, and quality and safety of the agricultural products are monitored.
The prior art is also as an agricultural product safety traceability system disclosed in the Chinese invention patent application with the application publication number of CN115796896A, and the agricultural product safety traceability system is characterized in that a planting base traceability module, a input product traceability module, an agricultural operation traceability module, a quality management traceability module, a storage process traceability module, a processing and packaging traceability module and a sales circulation traceability module are arranged, so that data information from production to sales of a supply chain is recorded and stored, meanwhile, a product source can be traced quickly, a responsibility main body is positioned, and the unqualified products can be identified and removed in a monitoring system to avoid the unqualified products from flowing into the market.
Aiming at the two technical schemes, the construction level of the traceability information is obviously mainly focused, so that the comprehensiveness of the traceability information is ensured, a certain defect exists in the attention degree of the agricultural product production body planting management, and the following aspects are particularly shown: 1. the method is mainly used for analyzing and supervising the planting condition according to the current tracking data, and comprehensively evaluating the past planting traceability information of a planting area is not combined, so that the effectiveness of agricultural product production quality analysis cannot be ensured, and further the effect and reliability of agricultural product planting quality management cannot be ensured.
2. The planting management of the subsequent agricultural products is not further tracked, so that the planting traceability information of the agricultural products cannot be expanded, the problems in the planting process cannot be found and solved in time, the optimization effect of the subsequent planting of the agricultural products cannot be improved, and the information obtained in the production process cannot be effectively applied to the planting link to form a complete information closed loop. Meanwhile, the improved auxiliary effect on the subsequent planting strategy is not obvious, and the flexibility and pertinence of the agricultural product planting structure cannot be improved.
Disclosure of Invention
In view of this, in order to solve the problems set forth in the above background technology, a system and a method for agricultural product planting traceability analysis and management based on big data are provided.
The aim of the invention can be achieved by the following technical scheme: the first aspect of the invention provides an agricultural product planting traceability analysis management system based on big data, which comprises: and the peanut planting data extraction module is used for extracting planting data and processing record data of the peanuts in the target area in the current planting period.
The historical traceability information extraction module is used for extracting traceability information of peanuts in the target area in various historical planting periods.
The peanut planting state analysis module is used for analyzing the current peanut planting quality in the target area according to the processing data of the peanuts in the target area in the current planting period to obtain the current peanut planting quality fitness。
The plant quality traceability analysis module is used for uniformly analyzing the peanut planting quality fitness of various planting periods according to the analysis mode corresponding to the current peanut planting quality fitness in the target area and confirming the abnormal peanut planting quality in the target area。
Peanut planting adjustment judging module for based onAndAnd judging whether the planting in the next planting period needs to be adjusted or not.
And the peanut planting adjustment confirming module is used for confirming recommended planting adjustment information in the next planting period when the judgment result is yes.
And the planting information base is used for storing the reference oil output corresponding to the weight of the unit peanut in the target area and storing the proper oil-pressing granularity interval of the peanut.
And the peanut planting adjustment feedback terminal is used for feeding back recommended planting adjustment information of the peanuts in the target area in the next planting period to peanut planting management staff in the target area.
The second aspect of the invention provides a method for agricultural product planting traceability analysis and management based on big data, which comprises the following steps: step 1, extracting peanut planting data: and extracting planting data and processing record data of the peanuts in the target area in the current planting period.
Step 2, extracting historical traceability information: and extracting traceability information of peanuts in the target area in various planting periods.
Step 3, analyzing the planting state of the peanuts: analyzing the current peanut planting quality in the target area to obtain the current peanut planting quality fitness。
Step 4, planting quality traceability analysis: the peanut planting quality fitness of various planting periods in the history is analyzed in a similar way according to the analysis mode corresponding to the current peanut planting quality fitness in the target area, and abnormal peanut planting quality in the target area is confirmed。
Step 5, peanut planting adjustment judgment: and judging whether the planting in the next planting period needs to be adjusted or not.
Step 6, peanut planting adjustment confirmation: and when the judgment result is yes, confirming recommended planting adjustment information in the next planting period.
Step 7, peanut planting adjustment feedback: and feeding back recommended planting adjustment information of the peanuts in the target area in the next planting period to peanut planting management staff in the target area.
Compared with the prior art, the invention has the following beneficial effects: (1) According to the invention, the current planting state analysis and the planting adjustment judgment are carried out according to the planting data and the processing record data in the current planting period and the traceability information in various historical planting periods, and the recommended planting adjustment information is confirmed, so that the further tracking treatment of subsequent peanut planting management is realized, the problem of insufficient attention of current planting management on an agricultural product production body is effectively solved, the management effect and the management reliability of subsequent peanut planting quality are ensured, the planting traceability information of agricultural products is expanded, the problems in the peanut planting process are conveniently and timely found and solved, the optimization effect of subsequent peanut planting is further improved, the utilization rate of the peanut planting and production process information is ensured, and the complete information closed loop of the production process information and the planting link is realized.
(2) According to the invention, the reference oil output is set by combining the traceability information in various planting periods of the history, so that the current peanut planting quality fitness analysis is carried out, the defect of comprehensive evaluation of the previous planting traceability information of the current uncombined planting area is avoided, the defect of carrying out the planting condition analysis only according to the current tracking data is overcome, the effectiveness of peanut planting quality analysis is ensured, and the reasonability of subsequent peanut planting quality abnormality assessment is facilitated.
(3) According to the invention, the oil output interference factor is set according to the particle size distribution rule and the water content of the peanuts, and the reference oil output quantity is set by combining the oil output conditions in different planting periods of the histories, so that the oil output quantity can be predicted more accurately, the over-high or over-low estimation is avoided, the more accurate reference oil output quantity is further established, and the authenticity and the reference of the follow-up peanut planting quality fitness analysis result are also ensured.
(4) According to the invention, the soil state deviation degree is analyzed by constructing the tracking numerical curve corresponding to each current soil tracking item, and the recommended planting adjustment information is confirmed according to the soil state deviation degree, so that not only can accurate adjustment suggestions be provided for the planting of peanuts in a target area, but also the grower can be helped to know the health condition of the soil and discover problems in time and take measures, meanwhile, the soil state is fully combined and displayed at present, unnecessary fertilization and watering are avoided, the agricultural production cost is saved, the auxiliary effect for improving the follow-up planting strategy is ensured, and the flexibility and pertinence of the peanut planting structure setting are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the connection of the modules of the system of the present invention.
FIG. 2 is a flow chart of the steps of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides an agricultural product planting traceability analysis management system based on big data, which comprises a peanut planting data extraction module, a historical traceability information extraction module, a peanut planting state analysis module, a planting quality traceability analysis module, a peanut planting adjustment judgment module, a peanut planting adjustment confirmation module, a planting information base and a peanut planting adjustment feedback terminal.
The peanut planting state analysis module is connected with the peanut planting data extraction module, the historical traceability information extraction module, the planting quality traceability analysis module, the peanut planting adjustment judgment module and the planting information base respectively, the planting quality traceability analysis module is also connected with the peanut planting adjustment judgment module, and the peanut planting adjustment confirmation module is connected with the peanut planting adjustment judgment module, the peanut planting adjustment feedback terminal, the peanut planting data extraction module and the historical traceability information extraction module respectively.
The peanut planting data extraction module is used for extracting planting data and processing record data of peanuts in a current planting period in a target area.
Specifically, the planting data comprises, but is not limited to, extracting the corresponding record value of each soil tracking item in each tracking day, and the processing record data comprises the corresponding record data of each current processing link, wherein the processing links comprise, but are not limited to, a screening link, a detection link and an oil extraction link.
Further, the recorded data of the sifting session includes, but is not limited to, the recorded total peanut weight and the peanut sifting weight.
Further, the recorded data of the detection link includes, but is not limited to, the amount of peanut distributed in each granularity section of the record and the average water content of the peanut.
Further, the recorded data of the oil extraction link includes, but is not limited to, the recorded oil yield and the total weight of the peanut.
In one particular embodiment, soil tracking items include, but are not limited to, soil temperature, soil moisture, soil pH, soil conductivity, soil nitrogen content, and soil phosphorus content.
In one embodiment, the specific detection process of the peanut amount distributed in each granularity section and the average peanut water content in the detection link is as follows: and transmitting the peanuts to be detected to a peanut detection area, and checking the granularity of the peanuts to be detected through a laser scanner in the peanut detection area, wherein the detected granularity value of the peanuts and the distributed peanut quantity corresponding to each detected granularity value of the peanuts are detected.
Comparing and classifying the detected granularity values with the set granularity intervals to obtain the detected granularity values in the granularity intervals, and further extracting the distributed peanut amount corresponding to the detected granularity values in the granularity intervals.
And detecting the water content of the peanuts to be detected through an infrared moisture detector in the peanut detection area, obtaining detected water content values, removing the maximum water content and the minimum water content of the detected water content values, and carrying out average calculation on the residual water content values after removal, wherein the calculation result is the average water content of the peanuts.
The history traceability information extraction module is used for extracting traceability information of peanuts in the target area in various planting periods.
In particular, the traceability information includes, but is not limited to, planting data and process record data.
The peanut planting state analysis module is used for analyzing the current peanut planting quality in the target area according to the processing data of the peanuts in the target area in the current planting period to obtain the current peanut planting quality fitness。
Illustratively, analyzing the current peanut planting quality in the target area includes: a1, extracting record data corresponding to each current processing link from the processing record data in the current planting period.
A2, extracting the total weight of the peanuts and the screening weight of the peanuts, which are recorded corresponding to the screening links, from the recorded data corresponding to the current processing links, taking the ratio of the screening weight of the peanuts to the total weight of the peanuts as the screening rate of the peanuts in the current planting period, and recording as。
A3, extracting the distributed peanut quantity and the average peanut water content in each granularity interval recorded corresponding to the detection link from the recorded data corresponding to each current processing link, setting the peanut oil-out interference factor according to the peanut quantity and the average peanut water content, and recording the peanut oil-out interference factor as。
A4, extracting oil output quantity recorded corresponding to the oil pressing link from the recorded data corresponding to each current processing link, and recording as。
A5, setting a reference oil output of the peanuts planted in the target area according to the traceability information of the peanuts in the target area in various planting periods, and marking as。
A6, counting the quality fitness of the current peanut planting，，In order to set the allowable floating oil yield,To round down the symbol.
Further, the step A3 sets a peanut oil extraction interference factor, including: q1, summing the distributed peanut amounts in each granularity interval, taking the sum result as a reference distributed peanut amount, and marking asCounting the number of granularity intervals with peanut amount not being 0, and recording as。
Q2, taking the ratio of the peanut quantity distributed in each particle size interval to the peanut quantity distributed in a reference mode as the peanut quantity percentage of each particle size interval, taking the particle size interval as an abscissa and the peanut quantity percentage as an ordinate, constructing a peanut particle size distribution curve, and analyzing the uniformity of the peanut particle size distribution。
Understandably, analyzing peanut particle size distribution uniformity includes: q21, extracting peak point number and valley point number from peanut size distribution curve, respectively recording asAndSimultaneously extracting the peanut percentage difference between each peak point and the adjacent valley value, and screening out the maximum peanut percentage difference from the peanut percentage difference, and recording the maximum peanut percentage difference as。
Q22, statistics of peanut particle size distribution uniformity，，To set the reference peak-valley point number,To set the allowable peanut weight percentage difference.
Q3, extracting a peanut proper oil extraction granularity interval from a planting information base, and counting the quantity of the distributed peanuts positioned in the peanut proper oil extraction granularity interval to be used as the peanut proper oil extraction quantity。
The specific statistical method for counting the peanut quantity distributed in the region of the peanut suitable for oil pressing grains is as follows: and X1, marking each particle size interval and the particle size interval of the peanut suitable residual oil on a numerical axis respectively.
And X2, if a certain granularity interval is completely positioned in the granularity interval of the peanut suitable residual oil, taking the granularity interval as an identical interval.
And X3, if a certain granularity section is partially positioned in the granularity section of the peanut suitable residual oil, marking the granularity section as a partially matched section.
X4, extracting the area of the part of the fit interval in the granularity interval of the peanut suitable residual oil, comparing the area with the marked area of the part of the fit interval on the numerical axis, marking the ratio as fit ratio, and taking the product of the peanut quantity distributed in the part of the fit interval and the fit ratio as the fit distribution peanut quantity of the part of the fit interval.
And X5, summing the distributed peanut quantity of each fit interval and the fit distributed peanut quantity of each part fit interval to obtain the distributed peanut quantity in the peanut proper oil-pressing granularity interval.
Q4, recording the average water content of the peanut asSetting peanut oil-out interference factors，，The distribution is to set a proper number of distribution granularity intervals and a difference of the number of the distribution granularity intervals,In order to set proper water content and water content deviation of peanut oil extraction,To set a reference distribution uniformity.
In general, peanuts in a suitable oil extraction particle size range have a high possibility of corresponding to a high oil yield, i.e., the higher the distribution of peanuts in the suitable oil extraction particle size range is, the higher the guarantee of the oil yield is, especially in a multi-particle size range. I.e.The larger the relative amount, the more the oil yield is rich, andThe smaller the relative, the greater the oil disturbances.
It should be noted that, in general, even distribution of peanuts in multiple granularity regions may be more beneficial to oil extraction, and when peanuts are distributed in multiple granularity regions, peanuts with different sizes can fully fill the oil extraction equipment, so that the oil extraction rate is improved. If the peanut is mainly concentrated in a single granularity interval, the utilization rate of oil pressing equipment is possibly reduced, the oil pressing effect is affected, and the peanut is uniformly distributed in a plurality of granularity intervals, so that the oil pressing process is more uniform and stable. Thus (2)And (3) withThe greater the difference andThe smaller the oil-out disturbance is, the larger the oil-out disturbance is.
It should be noted that the moisture content of peanuts affects the oil extraction efficiency during processing. Too high or too low a water content may lead to a decrease in the oil output, and thereforeThe larger the difference value of the oil output interference is, the larger the oil output interference is, and then the interference factor setting is carried out from four parameter dimensions of proper particle size distribution ratio, particle size distribution interval number, particle size distribution uniformity and peanut water content, so that the accuracy and reliability of the reference oil output setting are improved.
According to the embodiment of the invention, the oil output interference factor is set according to the particle size distribution rule and the water content of the peanuts, and the reference oil output quantity is set by combining the oil output conditions in different planting periods of the histories, so that the oil output quantity can be predicted more accurately, the over-high or over-low estimation is avoided, the more accurate reference oil output quantity is further established, and the authenticity and the referential of the follow-up peanut planting quality fitness analysis result are also ensured.
Further, in the step A4, setting a reference oil output of the peanuts planted in the target area, including: a41, taking the difference between the total weight of the peanuts and the peanut screening weight as the peanut screening residual weight, and recording asExtracting a reference oil output corresponding to the weight of the unit peanut in the target area from the planting information base, and marking the reference oil output asWill beThe reference oil output is recorded as。
A42, extracting oil output and total weight of the peanuts recorded in the corresponding oil pressing links in the various planting periods of the history from the traceable information in the various planting periods of the peanuts in the target area, and respectively recording the oil output and the total weight of the peanuts asAndCounting corresponding oil deviation in various planting periods of history，，The number of the planting period of the history is represented,。
A43, the number of the historical planting periods with the statistical oil deviation value being more than or equal to 0 and the number of the historical planting periods with the oil output deviation value being less than are respectively recorded asAnd。
A44, matching and comparing the peanut oil extraction interference factors with the reference loss oil extraction ratios corresponding to the set peanut oil extraction interference factor intervals to obtain the matching reference loss oil extraction ratios corresponding to the oil extraction interference factors, and marking the matching reference loss oil extraction ratios as。
A45, ifCalculating the average value of the oil outlet deviation values corresponding to various planting periods of history with the oil outlet deviation value larger than 0, and recording the calculated result asAnd then willAs a reference oil output for peanut planting in the target area.
A46, ifCalculating the average value of the oil-out deviation values corresponding to the various planting periods with the oil-out deviation value smaller than or equal to 0, and recording the calculated result asAnd then willAs a reference oil output for peanut planting in the target area.
It is added that whenIndicating that the historical oil output is higher, namely summing the reference oil output and the oil output deviation, whenIndicating that the historical oil output is low, i.e. the absolute value of the reference oil output and the oil output deviation is differed.
According to the embodiment of the invention, the reference oil output is set by combining the traceability information in various planting periods of the history, so that the current peanut planting quality fitness analysis is performed, the defect of comprehensive evaluation of the previous planting traceability information of the current uncombined planting area is avoided, the defect of performing planting condition analysis only according to the current tracking data is overcome, the effectiveness of peanut planting quality analysis is ensured, and the reasonability of subsequent peanut planting quality abnormality evaluation is facilitated.
The plant quality traceability analysis module is used for analyzing the peanut plant quality fitness of various planting periods in the history in a same way according to the analysis mode corresponding to the current peanut plant quality fitness in the target area and confirming the peanut plant quality abnormal degree in the target area。
Specifically, the determining the abnormal degree of the peanut planting quality in the target area comprises the following steps: n1, integrating the matching degree of the peanut planting quality of various historical planting periods with the matching degree of the current peanut planting quality to obtain the matching degree of the peanut planting quality of various current accumulated planting periods, comparing the matching degree of the peanut planting quality with the matching degree of the set reference peanut planting quality, counting the accumulated planting period number smaller than the matching degree of the set reference peanut planting, and recording asAt the same time, the accumulated planting period number is recorded as。
N2, constructing a peanut planting fitness curve by taking the accumulated planting period as an abscissa and the peanut planting quality fitness as an ordinate, and extracting the slope from the curve, and marking the curve as。
The slope extracted from the peanut planting fitness curve is the slope of the regression line corresponding to the peanut planting fitness curve.
N3, counting abnormal degree of peanut planting quality in target area，，Respectively set abnormal evaluation conditions of the planting quality of each peanut,Representation ofOr alternativelyIt is true that the method is that,Representation ofAndAt the same time, it is true that,Representation ofAndAt the same time, it is true that,And setting the change rate of the planting fitness of the peanuts for reference.
The peanut planting adjustment judging module is used for being based onAndAnd judging whether the planting in the next planting period needs to be adjusted or not.
Specifically, determining whether the planting in the next planting cycle requires adjustment includes: if it isOr alternativelyWill be the result of judging whether the planting in the next planting period is required to be adjusted,To set the quality of the reference plants.
If it isAnd is also provided withAnd judging whether the planting in the next planting period needs to be regulated or not.
And the peanut planting adjustment confirming module is used for confirming recommended planting adjustment information in the next planting period when the judgment result is yes.
Specifically, confirming recommended planting adjustment information in a next planting period includes: and V1, extracting corresponding record values of each soil tracking item in each tracking day from planting data of the peanuts in the target area in the current planting period.
And V2, constructing a tracking numerical curve corresponding to each current soil tracking item by taking the tracking day as an abscissa and taking the recorded numerical value as an ordinate.
And V3, taking each historical planting period with the peanut planting quality fitness larger than 0 as each reference planting period, and constructing the tracking numerical curve of each reference planting period corresponding to each soil tracking item in the same way according to the construction mode of the tracking numerical curve of each current soil tracking item.
V4, analyzing the deviation degree corresponding to each soil tracking item, screening the maximum value from the deviation degree, and recording the maximum value as the soil state deviation degree。
V5, ifTaking the soil environment regulation and control as the adjustment planting category of the next period, taking each soil tracking item with the deviation degree larger than 0 as a key adjustment factor, taking the adjustment planting category and the key adjustment factor as recommended planting adjustment information, ifAnd is also provided withAnd taking the replacement planting variety as recommended planting adjustment information.
Further, in the step V4, analyzing the deviation degree corresponding to each soil tracking item, including: v41, performing coincidence comparison on the tracking numerical curve corresponding to each current soil tracking item and the tracking numerical curve corresponding to each soil tracking item of each reference planting period to obtain the coincidence ratio of the tracking numerical curve corresponding to each soil tracking item of each reference period, obtaining the average coincidence ratio corresponding to each soil tracking item through mean value calculation, and recording as，The number of the soil-following item is indicated,。
The process for obtaining the trace numerical curve coincidence ratio of each soil trace item corresponding to each reference period is as follows: extracting the superposition length of the tracking numerical curve of each current soil tracking item and each soil tracking item corresponding to each reference planting period and the tracking numerical curve length of each soil tracking item corresponding to each reference planting period; and taking the ratio of the superposition length of the tracking numerical curve corresponding to each soil tracking item in each reference planting period to the length of the tracking numerical curve as the superposition ratio of the tracking numerical curve corresponding to each soil tracking item in each reference period.
V42, counting that the trace value curve coincidence ratio corresponding to each soil trace item is smaller than the set reference coincidence ratioIs recorded as the reference planting period number。
V43, counting the number of reference planting periods, and recording asFurther, the deviation degree corresponding to each soil tracking item is counted，。
According to the embodiment of the invention, the soil state deviation degree is analyzed by constructing the tracking numerical curve corresponding to each current soil tracking item, and the recommended planting adjustment information is confirmed according to the soil state deviation degree, so that not only can accurate adjustment suggestions be provided for the planting of peanuts in a target area, but also the growers can be helped to know the health condition of the soil and timely find out problems and take measures, meanwhile, the soil state is fully combined and displayed at present, unnecessary fertilization and watering are avoided, the agricultural production cost is saved, the auxiliary effect for improving the follow-up planting strategy is ensured, and the flexibility and pertinence of the peanut planting structure setting are improved.
The planting information base is used for storing the reference oil output corresponding to the weight of the peanuts in the target area and storing the granularity interval of the peanuts suitable for oil pressing.
And the peanut planting adjustment feedback terminal is used for feeding back recommended planting adjustment information of the peanuts in the target area in the next planting period to peanut planting management staff in the target area.
According to the embodiment of the invention, the current planting state analysis and the planting adjustment judgment are carried out according to the planting data and the processing record data in the current planting period and the traceability information in various historical planting periods, and the recommended planting adjustment information is confirmed, so that the further tracking treatment of subsequent peanut planting management is realized, the problem of insufficient attention to the planting management of the agricultural product production body is further effectively solved, the management effect and the management reliability of the subsequent peanut planting quality are ensured, the planting traceability information of the agricultural product is expanded, the problem in the peanut planting process is conveniently found and solved in time, the optimization effect of subsequent peanut planting is further improved, the utilization rate of the peanut planting and production process information is ensured, and the complete information closed loop of the production process information and the planting link is realized.
Referring to fig. 2, the invention provides a method for analyzing and managing agricultural product planting traceability based on big data, which comprises the following steps: step 1, extracting peanut planting data: and extracting planting data and processing record data of the peanuts in the target area in the current planting period.
Step 2, extracting historical traceability information: and extracting traceability information of peanuts in the target area in various planting periods.
Step 3, analyzing the planting state of the peanuts: analyzing the current peanut planting quality in the target area to obtain the current peanut planting quality fitness。
Step 4, planting quality traceability analysis: the peanut planting quality fitness of various planting periods in the history is analyzed in a similar way according to the analysis mode corresponding to the current peanut planting quality fitness in the target area, and abnormal peanut planting quality in the target area is confirmed。
Step 5, peanut planting adjustment judgment: and judging whether the planting in the next planting period needs to be adjusted or not.
Step 6, peanut planting adjustment confirmation: and when the judgment result is yes, confirming recommended planting adjustment information in the next planting period.
Step 7, peanut planting adjustment feedback: and feeding back recommended planting adjustment information of the peanuts in the target area in the next planting period to peanut planting management staff in the target area.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (7)
1. Agricultural product planting traceability analysis management system based on big data, which is characterized in that: the system comprises:
The peanut planting data extraction module is used for extracting planting data and processing record data of peanuts in the target area in the current planting period;
the historical traceability information extraction module is used for extracting traceability information of peanuts in the target area in various historical planting periods;
The peanut planting state analysis module is used for analyzing the current peanut planting quality in the target area according to the processing data of the peanuts in the target area in the current planting period to obtain the current peanut planting quality fitness ；
The plant quality traceability analysis module is used for uniformly analyzing the peanut planting quality fitness of various planting periods according to the analysis mode corresponding to the current peanut planting quality fitness in the target area and confirming the abnormal peanut planting quality in the target area；
Peanut planting adjustment judging module for based onAndJudging whether the planting in the next planting period requires adjustment or not;
the peanut planting adjustment confirming module is used for confirming recommended planting adjustment information in the next planting period when the judgment result is yes;
The planting information base is used for storing a reference oil output corresponding to the weight of the unit peanut in the target area and storing a granularity section of the peanut suitable for oil extraction;
The peanut planting adjustment feedback terminal is used for feeding back recommended planting adjustment information of the peanuts in the target area in the next planting period to peanut planting management staff in the target area;
The analyzing the current peanut planting quality in the target area comprises the following steps:
extracting record data corresponding to each current processing link from the processing record data in the current planting period;
extracting total peanut weight and peanut screening weight recorded correspondingly in the screening links from recorded data corresponding to the current processing links, taking the ratio of the peanut screening weight to the total peanut weight as the peanut screening rate in the current planting period, and recording as ；
Extracting the distributed peanut quantity and the average peanut water content in each granularity interval recorded by the detection links from the recorded data corresponding to each current processing link, setting the peanut oil extraction interference factor according to the peanut quantity and the average peanut water content, and recording as；
Extracting oil output quantity recorded corresponding to the oil pressing link from the recorded data corresponding to each current processing link, and recording as；
Setting a reference oil output of peanuts planted in the target area according to the traceability information of the peanuts in the target area in various planting periods, and marking the reference oil output as；
Counting the quality fitness of current peanut planting，，In order to set the allowable floating oil yield,Rounding down the symbol;
The determining the abnormal degree of the peanut planting quality in the target area comprises the following steps:
Integrating the matching degree of the peanut planting quality of the historical various planting periods with the matching degree of the current peanut planting quality to obtain the matching degree of the peanut planting quality of the current various accumulated planting periods, comparing the matching degree of the peanut planting quality with the matching degree of the set reference peanut planting quality, counting the accumulated planting period number smaller than the matching degree of the set reference peanut planting, and recording as At the same time, the accumulated planting period number is recorded as；
Constructing a peanut planting fitness curve by taking the accumulated planting period as an abscissa and the peanut planting quality fitness as an ordinate, and extracting the slope from the curve to be recorded as；
Counting abnormal peanut planting quality degree in target area，，Respectively set abnormal evaluation conditions of the planting quality of each peanut,Representation ofOr alternativelyIt is true that the method is that,Representation ofAndAt the same time, it is true that,Representation ofAndAt the same time, it is true that,The change rate of the planting fitness of the peanuts for setting reference;
Judging whether the planting of the next planting period is required to be adjusted or not includes:
If it is Or alternativelyWill be the result of judging whether the planting in the next planting period is required to be adjusted,Setting the consistency of the reference planting quality;
If it is And is also provided withAnd judging whether the planting in the next planting period needs to be regulated or not.
2. The agricultural product planting traceability analysis and management system based on big data as set forth in claim 1, wherein: the reference oil output amount for planting peanuts in the set target area comprises the following steps:
Taking the difference between the total weight of the peanuts and the screening weight of the peanuts as the residual screening weight of the peanuts, and recording as Extracting a reference oil output corresponding to the weight of the unit peanut in the target area from the planting information base, and marking the reference oil output asWill beThe reference oil output is recorded as；
Extracting oil output and total weight of the peanuts recorded in the corresponding oil pressing links in various planting periods of the history from traceable information in various planting periods of the peanuts in the target area, and respectively recording the oil output and the total weight of the peanuts asAndCounting corresponding oil deviation in various planting periods of history，，The number of the planting period of the history is represented,；
Counting the number of historical planting periods with oil deviation greater than or equal to 0 and the number of historical planting periods with oil deviation less than 0, and respectively recording asAnd；
Matching and comparing the peanut oil extraction interference factors with the reference loss oil extraction ratios corresponding to the set peanut oil extraction interference factor intervals to obtain the matching reference loss oil extraction ratios corresponding to the oil extraction interference factors, and recording as；
If it isCalculating the average value of the oil outlet deviation values corresponding to various planting periods of history with the oil outlet deviation value larger than 0, and recording the calculated result asAnd then willAs a reference oil output of peanuts planted in the target area;
If it is Calculating the average value of the oil-out deviation values corresponding to the various planting periods with the oil-out deviation value smaller than or equal to 0, and recording the calculated result asAnd then willAs a reference oil output for peanut planting in the target area.
3. The agricultural product planting traceability analysis and management system based on big data as set forth in claim 1, wherein: the set peanut oil extraction interference factor comprises:
summing the peanut amount distributed in each granularity interval, and recording the sum result as reference peanut amount Counting the number of granularity intervals with peanut amount not being 0, and recording as；
Taking the ratio of the peanut quantity distributed in each particle size interval to the peanut quantity distributed in a reference mode as the peanut quantity percentage of each particle size interval, taking the particle size interval as an abscissa and the peanut quantity percentage as an ordinate to construct a peanut particle size distribution curve, and analyzing the uniformity of the peanut particle size distribution；
Extracting a peanut proper oil extraction granularity interval from a planting information base, and counting the distributed peanut quantity in the peanut proper oil extraction granularity interval to be used as the proper oil extraction peanut quantity；
The average water content of the peanut is recorded asSetting peanut oil-out interference factors，，The distribution is to set a proper number of distribution granularity intervals and a difference of the number of the distribution granularity intervals,In order to set proper water content and water content deviation of peanut oil extraction,To set a reference distribution uniformity.
4. A big data based agricultural product planting traceability analysis and management system as set forth in claim 3, wherein: the analysis of peanut particle size distribution uniformity comprises:
Extracting the peak point number and the valley point number from the peanut particle size distribution curve, and respectively recording the peak point number and the valley point number as AndSimultaneously extracting the peanut percentage difference between each peak point and the adjacent valley value, and screening out the maximum peanut percentage difference from the peanut percentage difference, and recording the maximum peanut percentage difference as；
Counting the uniformity of peanut particle size distribution，，To set the reference peak-valley point number,To set the allowable peanut weight percentage difference.
5. The agricultural product planting traceability analysis and management system based on big data as set forth in claim 1, wherein: the identifying recommended planting adjustment information in a next planting period includes:
Extracting corresponding record values of each soil tracking item in each tracking day from planting data of peanuts in a target area in a current planting period;
Constructing a tracking numerical curve corresponding to each current soil tracking item by taking the tracking day as an abscissa and taking the recorded numerical value as an ordinate;
Taking historical various planting periods with the peanut planting quality fitness larger than 0 as various reference planting periods, and constructing tracking numerical curves of the various reference planting periods corresponding to the various soil tracking items in a similar way according to the construction mode of the tracking numerical curves of the current various soil tracking items;
Analyzing the deviation degree corresponding to each soil tracking item, screening the maximum value from the deviation degree as the soil state deviation degree, and recording as ；
If it isTaking the soil environment regulation and control as the adjustment planting category of the next period, taking each soil tracking item with the deviation degree larger than 0 as a key adjustment factor, taking the adjustment planting category and the key adjustment factor as recommended planting adjustment information, ifAnd is also provided withAnd taking the replacement planting variety as recommended planting adjustment information.
6. The agricultural product planting traceability analysis and management system based on big data according to claim 5, wherein: the analyzing the deviation degree corresponding to each soil tracking item comprises the following steps:
overlapping and comparing the tracking numerical curve corresponding to each current soil tracking item with the tracking numerical curve corresponding to each soil tracking item of each reference planting period to obtain the overlapping ratio of the tracking numerical curve corresponding to each soil tracking item of each reference period, calculating the average overlapping ratio corresponding to each soil tracking item by means of average value, and recording as ，The number of the soil-following item is indicated,；
Counting that the corresponding tracking numerical curve of each soil tracking item is smaller than the set reference matching ratioIs recorded as the reference planting period number；
Counting the number of reference planting periods, and recording asFurther, the deviation degree corresponding to each soil tracking item is counted，。
7. The agricultural product planting traceability analysis and management method based on big data is executed by the agricultural product planting traceability analysis and management system based on big data as set forth in claim 1, and is characterized in that: the method comprises the following steps:
Step 1, extracting peanut planting data: extracting planting data and processing record data of peanuts in a current planting period in a target area;
step 2, extracting historical traceability information: extracting traceability information of peanuts in the target area in various planting periods;
step 3, analyzing the planting state of the peanuts: analyzing the current peanut planting quality in the target area to obtain the current peanut planting quality consistency;
Step 4, planting quality traceability analysis: the quality fitness of the peanuts in various planting periods in the history is analyzed in a similar way according to an analysis mode corresponding to the current quality fitness of the peanuts in the target area, and abnormal quality of the peanuts in the target area is confirmed;
step 5, peanut planting adjustment judgment: judging whether the planting in the next planting period requires adjustment or not;
step 6, peanut planting adjustment confirmation: if the judgment result is yes, confirming recommended planting adjustment information in the next planting period;
Step 7, peanut planting adjustment feedback: and feeding back recommended planting adjustment information of the peanuts in the target area in the next planting period to peanut planting management staff in the target area.
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